Methods, devices, media and equipment for screening urban environmental noise sources
By employing two-dimensional Fourier transform and multi-stage clustering analysis, surface wave windows with coherent characteristics are screened out, solving the problem that noise source screening in existing technologies is not applicable to urban environments, and improving noise source identification and imaging quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INST OF MINERAL RESOURCES CHINA METALLURGICAL GEOLOGY ADMINISTRATION
- Filing Date
- 2025-03-20
- Publication Date
- 2026-07-17
AI Technical Summary
Existing noise source screening methods are not suitable for complex urban environments and cannot effectively characterize the spatial coherence of the background noise field, resulting in inaccurate noise source screening.
Two-dimensional Fourier transform and median truncation were used to process the noise waveform. Surface wave windows with coherent characteristics and different modes were screened through multi-stage cluster analysis. Imaging was then performed using seismic interferometry and phase shifting methods.
It improves the ability to refine the characterization and screening of noise sources in urban environments, enhances the quality of passive source surface wave imaging, and strengthens the identification and understanding of complex noise sources.
Smart Images
Figure CN120294838B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of seismic exploration technology, and in particular to a method, apparatus, medium and equipment for screening urban environmental noise sources. Background Technology
[0002] The vibrations on the Earth's surface caused by natural phenomena and human activities constitute the background noise signal in continuous seismic records. By analyzing these background noises, we can understand the geological structure beneath the city, providing a basis for urban construction planning. We can also monitor the temporal changes of underground media, such as changes in groundwater and the dynamic changes of oil and gas reservoirs, which is of great significance for resource exploration and development.
[0003] Background noise generated by human activities is mainly concentrated in cities. Existing methods for processing urban background noise data in seismic records incorporate noise source screening strategies during the processing to identify coherent noise sources. These selected noise sources are then used for underground space exploration and energy and mineral exploration. Current noise source screening methods primarily rely on frequency domain screening based on the statistical characteristics of diffuse wave fields. This method divides the seismic waveform sequence into multiple non-overlapping short-time windows, performs statistical analysis on the frequency domain data of all sub-windows, calculates three dimensionless physical quantities, quantifies the residuals between these three physical quantities and the target object, calculates the P-value, and then uses the P-value to screen diffuse waveforms.
[0004] Since frequency domain screening methods based on the statistical characteristics of diffuse wave fields mainly rely on waveforms recorded by a single station, they cannot characterize the spatial coherence of the background noise field. Furthermore, in urban environments, ballistic waves, which dominate traffic noise, have inherent coherence. Therefore, frequency domain screening methods based on the statistical characteristics of diffuse wave fields are not suitable for screening noise sources in complex urban environments. A noise source screening method suitable for urban environments is needed. Summary of the Invention
[0005] In view of this, the present invention provides a method, apparatus, medium and equipment for screening urban environmental noise sources, the main purpose of which is to solve the problem that existing noise source screening methods are not applicable to complex urban environments.
[0006] According to one aspect of this application, a method for screening urban environmental noise sources is provided, the method comprising:
[0007] Acquire the multichannel background noise waveform of the work area to be measured, and slice the multichannel background noise waveform according to a preset time window to obtain multiple sub-window waveforms;
[0008] A two-dimensional Fourier transform is performed on each of the sub-window waveforms to obtain multiple data in the first frequency-wavenumber domain. Based on the median truncation method, each data in the first frequency-wavenumber domain is normalized to obtain multiple data in the second frequency-wavenumber domain.
[0009] Multi-stage clustering processing was performed on data from multiple second frequency-wavenumber domains to obtain multiple surface wave windows with coherent characteristics and different modes.
[0010] Optionally, the median truncation method uses the following formula:
[0011]
[0012] Where d′ and d represent the frequency-wavenumber domain data before and after median truncation and normalization, respectively; μ and σ represent the median and standard deviation of the data, respectively; and n represents a constant.
[0013] Optionally, the multi-stage clustering processing of multiple second frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes includes:
[0014] The data in the multiple second frequency-wavenumber domains are subjected to a first-stage clustering process. Based on the clustering results of the first stage, the frequency band range of the waveforms with coherent characteristics is determined.
[0015] Based on the frequency band range, the data in the multiple second frequency-wavenumber domains are filtered, and the filtered frequency-wavenumber domain data are subjected to a second-stage clustering process. Based on the clustering results of the second stage, multiple surface wave windows with coherent characteristics are obtained.
[0016] A third-stage clustering process is performed on the multiple surface waves with coherent modes. Based on the clustering results of the third stage, multiple surface wave windows with coherent characteristics and different modes are obtained.
[0017] Optionally, the method for screening urban environmental noise sources further includes:
[0018] The clustering process is divided into three stages: first stage, second stage, and third stage, based on different preset clustering models.
[0019] Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.
[0020] Optionally, after performing multi-stage clustering processing on multiple second frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes, the method for screening urban environmental noise sources further includes:
[0021] Seismic interferometry is performed on each surface wave window with coherent characteristics and different modes to obtain the virtual shot gather corresponding to each surface wave window;
[0022] Multiple virtual gun sets are superimposed to obtain superimposed virtual gun set data;
[0023] The superimposed virtual shot gather data is imaged using the phase-shifting method to obtain a dispersion energy map.
[0024] Optionally, before acquiring the multichannel background noise waveform of the work area to be measured, the method for screening urban environmental noise sources further includes:
[0025] Acquire initial background noise data at different measurement points in the work area to be measured, and convert the initial background noise data into noise data with a preset format;
[0026] The noise data with the preset format is subjected to mean removal and mean removal processing in sequence to obtain the multi-channel background noise waveform of the work area to be measured.
[0027] According to another aspect of this application, a device for screening urban environmental noise sources is provided, comprising:
[0028] The slicing module is used to acquire the multichannel background noise waveform of the work area to be measured, and to slice the multichannel background noise waveform according to a preset time window to obtain multiple sub-window waveforms.
[0029] The conversion module is used to perform a two-dimensional Fourier transform on each of the sub-window waveforms to obtain multiple data in the first frequency-wavenumber domain, and to perform normalization processing on each of the data in the first frequency-wavenumber domain based on the median truncation method to obtain multiple data in the second frequency-wavenumber domain.
[0030] The clustering module is used to perform multi-stage clustering processing on multiple second frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes.
[0031] Optionally, the median truncation method uses the following formula:
[0032]
[0033] Where d′ and d represent the frequency-wavenumber domain data before and after median truncation and normalization, respectively; μ and σ represent the median and standard deviation of the data, respectively; and n represents a constant.
[0034] Optionally, the clustering module is further configured to:
[0035] The data in the multiple second frequency-wavenumber domains are subjected to a first-stage clustering process. Based on the clustering results of the first stage, the frequency band range of the waveforms with coherent characteristics is determined.
[0036] Based on the frequency band range, the data in the multiple second frequency-wavenumber domains are filtered, and the filtered frequency-wavenumber domain data are subjected to a second-stage clustering process. Based on the clustering results of the second stage, multiple surface wave windows with coherent characteristics are obtained.
[0037] A third-stage clustering process is performed on the multiple surface waves with coherent modes. Based on the clustering results of the third stage, multiple surface wave windows with coherent characteristics and different modes are obtained.
[0038] Optionally, the clustering module is further configured to:
[0039] The clustering process is divided into three stages: first stage, second stage, and third stage, based on different preset clustering models.
[0040] Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.
[0041] Optionally, the urban environmental noise source screening device further includes:
[0042] The dispersion energy map generation module is used to perform seismic interferometry on each surface wave window with coherent characteristics and different modes to obtain a virtual shot gather corresponding to each surface wave window; multiple virtual shot gathers are superimposed to obtain superimposed virtual shot gather data; and the superimposed virtual shot gather data is imaged using the phase shift method to obtain a dispersion energy map.
[0043] Optionally, the urban environmental noise source screening device further includes:
[0044] The raw data acquisition module is used to acquire the initial background noise data of different measurement points in the work area to be measured, and convert the initial background noise data into noise data with a preset format.
[0045] The preprocessing module is used to sequentially perform mean removal and mean removal processing on the noise data with a preset format to obtain multi-channel background noise waveforms of the work area to be measured.
[0046] According to another aspect of this application, a computer device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;
[0047] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for screening urban environmental noise sources.
[0048] By employing the above-described technical solutions, the technical solutions provided by the embodiments of the present invention have at least the following advantages:
[0049] This application provides a method, apparatus, device, and medium for screening urban environmental noise sources. It performs a two-dimensional Fourier transform on the waveform of a sub-window to obtain frequency-wavenumber domain data. The frequency-wavenumber domain data is then normalized using the median truncation method. Multi-stage clustering is performed on the normalized frequency-wavenumber data to obtain multiple surface wave windows with coherent characteristics and different modes. From the perspective of the frequency-wavenumber domain, the spatial coherence of the array data is fully considered, and multi-stage clustering is performed on the data. This allows for refined characterization and screening of complex noise sources in urban environments, obtaining multiple surface wave windows with coherent characteristics and different modes, thereby improving the quality of passive source surface wave imaging.
[0050] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description
[0051] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:
[0052] Figure 1 A flowchart illustrating a method for screening urban environmental noise sources according to an embodiment of this application is shown;
[0053] Figure 2 The diagram illustrates a multichannel background noise waveform of a method for screening urban environmental noise sources according to an embodiment of this application.
[0054] Figure 3 Another flowchart of a method for screening urban environmental noise sources provided in an embodiment of this application is shown;
[0055] Figure 4 This paper shows a structural block diagram of another urban environmental noise source screening device provided in an embodiment of this application;
[0056] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention is shown.
[0057] in,
[0058] Figure 4In Chinese: 402 - Slicing module; 404 - Transformation module; 406 - Clustering module;
[0059] Figure 5 In Chinese: 502 - Processor; 504 - Communication interface; 506 - Memory; 508 - Communication bus; 510 - Program. Detailed Implementation
[0060] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.
[0061] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the specific embodiments, structures, features, and effects according to the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "an embodiment" or "an embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0062] To address the problem that current noise source screening methods are not applicable to urban environments, this application provides a method for screening urban environmental noise sources, such as... Figure 1 As shown, the method includes:
[0063] 102: Obtain the multi-channel background noise waveform of the work area to be measured, and slice the multi-channel background noise waveform according to the preset time window to obtain multiple sub-window waveforms;
[0064] 104: Perform a two-dimensional Fourier transform on the waveform of each sub-window to obtain multiple data in the first frequency-wavenumber domain. Normalize the data in each first frequency-wavenumber domain based on the median truncation method to obtain multiple data in the second frequency-wavenumber domain.
[0065] 106: Perform multi-stage clustering on multiple second-frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes.
[0066] Specifically, multiple channels of initial background noise data from different measurement points in the area to be measured are acquired. Due to differences in equipment, the format of the initial background noise data varies, such as .sac and .mat formats. This initial background noise data is then converted to noise data with a preset format. Preprocessing of the multiple channels of noise data with the preset format is performed, such as mean removal and potential reduction, to obtain the multiple channels of background noise waveforms for the area to be measured. Figure 2 As shown. Then, the continuous multi-channel background noise waveform is sliced according to the preset time window to obtain several short-time sub-window waveforms.
[0067] To capture the spatial coherence characteristics of the background noise field, a two-dimensional Fourier transform (frequency-wavenumber transform) is performed on the sliced sub-window waveform to obtain data in the frequency-wavenumber domain. The two-dimensional Fourier transform, by converting the signal to the frequency-wavenumber domain, allows for simultaneous analysis of the signal's temporal and spatial characteristics, thereby capturing the spatial coherence characteristics of the background noise field, highlighting the wave propagation patterns in space, enhancing the understanding of the wave field, and improving the array's ability to detect weak signals or complex wave fields. Therefore, performing a two-dimensional Fourier transform on the sub-window waveform can highlight the spatial coherence characteristics of the signal, further improving the accuracy of noise source extraction.
[0068] Since the original frequency-wavenumber domain data may contain outliers that mask weak coherent signals, the median truncation method is used to normalize the frequency-wavenumber domain data to avoid the influence of outliers. The normalized frequency-wavenumber domain data is then subjected to multi-stage clustering. Through multi-stage clustering, the clustering results of complex noise sources in urban environments are continuously refined, and noise segments with coherent characteristics and different patterns are selected.
[0069] This application provides a method for screening urban environmental noise sources. Compared with existing technologies, it performs a two-dimensional Fourier transform on the waveform of the sub-window to obtain data in the frequency-wavenumber domain. The frequency-wavenumber domain data is then normalized using the median truncation method. Multi-stage clustering is then performed on the normalized frequency-wavenumber domain data to obtain multiple surface wave windows with different modes and coherent characteristics. From the perspective of the frequency-wavenumber domain, the spatial coherence of the array data is fully considered, and multi-stage clustering is performed on the data. This allows for refined characterization and screening of complex noise sources in urban environments, obtaining multiple surface wave windows with coherent characteristics and different modes, thereby improving the quality of passive source surface wave imaging.
[0070] In one embodiment, the median truncation method uses the following formula:
[0071]
[0072] Where d′ and d represent the frequency-wavenumber domain data before and after median truncation and normalization, respectively; μ and σ represent the median and standard deviation of the data, respectively; and n represents a constant.
[0073] Specifically, the raw frequency-wavenumber domain data may contain outliers, which can mask weak coherent signals. Therefore, to highlight the useful information in the data, a median truncation algorithm is used to truncate the raw frequency-wavenumber domain data. This ensures that the data is distributed within a reasonable range, avoids the influence of outliers, and allows useful information to be further highlighted.
[0074] In another embodiment of the invention, for further definition and explanation, such as Figure 3 As shown, multi-stage clustering processing is performed on multiple second-frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes, including:
[0075] 302: Perform first-stage clustering on multiple second-frequency-wavenumber domain data, and determine the frequency band range of waveforms with coherent characteristics based on the first-stage clustering results;
[0076] 304: Based on the frequency band range, multiple second frequency-wavenumber domain data are filtered, and the filtered frequency-wavenumber domain data are subjected to a second-stage clustering process. Based on the second-stage clustering results, multiple surface wave windows with coherent characteristics are obtained.
[0077] 306: Perform a third-stage clustering process on multiple surface wave windows with coherent mode characteristics. Based on the clustering results of the third stage, multiple surface wave windows with coherent characteristics and different modes are obtained.
[0078] Specifically, the multi-stage deep clustering processing is based on a deep embedded clustering algorithm, and its core process consists of three steps: (a) The first stage of clustering, also known as the primary clustering stage, characterizes noise sources over a wide frequency band and identifies the temporal distribution patterns of typical noise sources. This stage separates the surface wave windows with coherent characteristics from the noise windows with incoherent characteristics by distinguishing the effective frequency-wavenumber domain windows of surface waves, and determines the frequency band range of waveforms with coherent characteristics. (b) The second stage of clustering, also known as the secondary clustering stage, first performs bandpass filtering on the frequency-wavenumber domain data based on the frequency band range of waveforms with coherent characteristics determined in the primary clustering stage. The filtered data then undergoes the second stage of clustering processing, using deep embedded clustering technology to extract coherent signals and selecting noise segments suitable for background noise imaging in urban environments for specific frequency-wavenumber domain windows. (c) The third stage of clustering, also known as the pattern-specific clustering stage, further subdivides the coherent signals obtained from the secondary clustering, further subdividing them according to the signal patterns, such as basic patterns, higher-order patterns, etc. Multi-stage deep clustering can enhance our understanding of noise sources in complex environments, help us understand the mechanisms of noise generation, and can be used across different scenarios. Furthermore, by continuously refining the clustering results of noise sources using multi-stage deep clustering algorithms, we can filter out noise segments containing different patterns.
[0079] In one embodiment, a first-stage clustering process, a second-stage clustering process, and a third-stage clustering process are performed based on different preset clustering models.
[0080] Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.
[0081] Specifically, training datasets for different stages are obtained, and clustering models corresponding to each stage are trained to obtain multiple trained clustering models. The data corresponding to each stage is then input into the corresponding trained clustering model to obtain the output data for that stage. Through multiple clustering operations, noise sources in the urban environment are further subdivided, ultimately resulting in surface wave windows with coherent characteristics and different patterns.
[0082] In one embodiment, after performing multi-stage clustering processing on multiple second frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes, the method for screening urban environmental noise sources further includes:
[0083] Seismic interferometry is performed on each surface wave window with coherent characteristics and different modes to obtain the virtual shot gather corresponding to each surface wave window;
[0084] Multiple virtual gun sets are superimposed to obtain superimposed virtual gun set data;
[0085] The phase-shifting method was used to image the superimposed virtual shot gather data to obtain a dispersion energy map.
[0086] Specifically, dispersive energy imaging is performed on surface wave windows with coherent characteristics and different modes obtained through different clustering stages. For example, interferometric imaging methods are used: seismic interferometry is performed on surface wave windows with coherent characteristics and different modes to obtain virtual shot gathers corresponding to noise segments. For example, virtual shot gathers are obtained by seismic interferometry using any of the methods of cross-correlation, cross-coherence, or deconvolution. Then, the virtual shot gathers are stacked, for example, using signal-to-noise ratio weighted stacking or phase-weighted stacking. Finally, the stacked virtual shot gather data is imaged using the phase-shifting method to obtain a high-quality dispersive energy map, which can reduce the interference of artifacts to a certain extent and highlight the information of higher-order surface waves.
[0087] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this embodiment of the invention provides a device for screening urban environmental noise sources, such as... Figure 4 As shown, the device includes:
[0088] The slicing module 402 is used to acquire the multi-channel background noise waveform of the work area to be measured, and to slice the multi-channel background noise waveform according to a preset time window to obtain multiple sub-window waveforms.
[0089] The conversion module 404 is used to perform a two-dimensional Fourier transform on the waveform of each sub-window to obtain multiple data in the first frequency-wavenumber domain. Based on the median truncation method, the data in each first frequency-wavenumber domain are normalized to obtain multiple data in the second frequency-wavenumber domain.
[0090] Clustering module 406 is used to perform multi-stage clustering processing on multiple second frequency-wavenumber domain data to obtain surface wave windows with coherent characteristics and different modes.
[0091] This application provides a device for screening urban environmental noise sources. Compared with the prior art, it performs a two-dimensional Fourier transform on the waveform of the sub-window to obtain data in the frequency-wavenumber domain. The data in the frequency-wavenumber domain is normalized by the median truncation method. The normalized frequency-wavenumber domain data is then subjected to multi-stage clustering to obtain multiple surface wave windows with different modes and coherent characteristics. From the perspective of the frequency-wavenumber domain, the spatial coherence of the array data is fully considered, and the data is subjected to multi-stage clustering. This allows for the refined characterization and screening of complex noise sources in urban environments, obtaining multiple surface wave windows with coherent characteristics and different modes, thereby improving the quality of passive source surface wave imaging.
[0092] In one embodiment, the median truncation method uses the following formula:
[0093]
[0094] Where d′ and d represent the frequency-wavenumber domain data before and after median truncation and normalization, respectively; μ and σ represent the median and standard deviation of the data, respectively; and n represents a constant.
[0095] In one embodiment, the clustering module is also used for:
[0096] The first stage of clustering is performed on multiple second frequency-wavenumber domain data. Based on the results of the first stage of clustering, the frequency band range of waveforms with coherent characteristics is determined.
[0097] Based on the frequency band range, multiple second frequency-wavenumber domain data are filtered, and the filtered frequency-wavenumber domain data are subjected to a second-stage clustering process. Based on the second-stage clustering results, multiple surface wave windows with coherent characteristics are obtained.
[0098] A third-stage clustering process is performed on multiple surface wave windows with coherent characteristics. Based on the results of the third-stage clustering, multiple surface wave windows with coherent characteristics but different modes are obtained.
[0099] In one embodiment, the clustering module is also used for:
[0100] The clustering process is divided into three stages: first stage, second stage, and third stage, based on different preset clustering models.
[0101] Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.
[0102] In one embodiment, the urban environmental noise source screening device further includes:
[0103] The dispersion energy map generation module is used to perform seismic interferometry on each surface wave window with coherent characteristics and different modes to obtain the virtual shot gather corresponding to each surface wave window; multiple virtual shot gathers are superimposed to obtain superimposed virtual shot gather data; the superimposed virtual shot gather data is imaged using the phase shift method to obtain the dispersion energy map.
[0104] In one embodiment, the urban environmental noise source screening device further includes:
[0105] The raw data acquisition module is used to acquire the initial background noise data of different measurement points in the work area to be measured, and convert the initial background noise data into noise data with a preset format.
[0106] The preprocessing module is used to sequentially perform mean removal and mean removal processing on noise data with a preset format to obtain multi-channel background noise waveforms of the work area to be measured.
[0107] According to one embodiment of the present invention, a storage medium is provided, the storage medium storing at least one executable instruction, the computer-executable instruction being able to execute the method for screening urban environmental noise sources in any of the above method embodiments.
[0108] Figure 5 The diagram illustrates a structural schematic of a computer device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computer device.
[0109] like Figure 5 As shown, the computer device may include: a processor 502, a communications interface 504, a memory 506, and a communications bus 508.
[0110] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508.
[0111] Communication interface 504 is used to communicate with other network elements such as clients or other servers.
[0112] The processor 502 is used to execute program 510, which can specifically execute the relevant steps in the above-described embodiment of the method for screening urban environmental noise sources.
[0113] Specifically, program 510 may include program code that includes computer operation instructions.
[0114] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computer device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.
[0115] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.
[0116] Specifically, program 510 can be used to cause processor 502 to perform the following operations:
[0117] Acquire multichannel background noise waveforms of the work area to be measured, and slice the multichannel background noise waveforms according to a preset time window to obtain multiple sub-window waveforms;
[0118] A two-dimensional Fourier transform is performed on the waveform of each sub-window to obtain multiple data in the first frequency-wavenumber domain. Based on the median truncation method, the data in each first frequency-wavenumber domain are normalized to obtain multiple data in the second frequency-wavenumber domain.
[0119] Multi-stage clustering processing was performed on data from multiple second frequency-wavenumber domains to obtain multiple surface wave windows with coherent characteristics and different modes.
[0120] It will be apparent to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. In one embodiment, they can be implemented using device-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular hardware and software combination.
[0121] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.
Claims
1. A method for screening urban environmental noise sources, characterized in that, include: Acquire the multichannel background noise waveform of the work area to be measured, and slice the multichannel background noise waveform according to a preset time window to obtain multiple sub-window waveforms; A two-dimensional Fourier transform is performed on each of the sub-window waveforms to obtain multiple data in the first frequency-wavenumber domain. Based on the median truncation method, each data in the first frequency-wavenumber domain is normalized to obtain multiple data in the second frequency-wavenumber domain. Multi-stage clustering processing was performed on multiple second frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes. The multi-stage clustering process performed on multiple second-frequency-wavenumber domain data yields multiple surface wave windows with coherent characteristics and different modes, including: The data in the multiple second frequency-wavenumber domains are subjected to a first-stage clustering process. Based on the clustering results of the first stage, the frequency band range of the waveforms with coherent characteristics is determined. Based on the frequency band range, the data in the multiple second frequency-wavenumber domains are filtered, and the filtered frequency-wavenumber domain data are subjected to a second-stage clustering process. Based on the clustering results of the second stage, multiple surface wave windows with coherent characteristics are obtained. A third-stage clustering process is performed on the multiple surface waves with coherent modes. Based on the clustering results of the third stage, multiple surface wave windows with coherent characteristics and different modes are obtained. After performing multi-stage clustering processing on multiple second-frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes, the method for screening urban environmental noise sources further includes: Seismic interferometry is performed on each surface wave window with coherent characteristics and different modes to obtain the virtual shot gather corresponding to each surface wave window; Multiple virtual gun sets are superimposed to obtain superimposed virtual gun set data; The superimposed virtual shot gather data is imaged using the phase-shifting method to obtain a dispersion energy map.
2. The method for screening urban environmental noise sources as described in claim 1, characterized in that, The formula used in the median truncation method is: in, This represents the frequency-wavenumber domain data after median truncation and normalization. This represents the frequency-wavenumber domain data before median truncation and normalization; and These represent the median and standard deviation of the data, respectively. Represents a constant.
3. The method for screening urban environmental noise sources as described in claim 1, characterized in that, The method for screening urban environmental noise sources also includes: The clustering process is divided into three stages: first stage, second stage, and third stage, based on different pre-defined clustering models. Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.
4. The method for screening urban environmental noise sources as described in any one of claims 1-3, characterized in that, Before acquiring the multichannel background noise waveform of the work area to be measured, the method for screening urban environmental noise sources further includes: Acquire initial background noise data at different measurement points in the work area to be measured, and convert the initial background noise data into noise data with a preset format; The noise data with the preset format is subjected to mean removal and mean removal processing in sequence to obtain the multi-channel background noise waveform of the work area to be measured.
5. A device for screening urban environmental noise sources, characterized in that, include: The slicing module is used to acquire the multichannel background noise waveform of the work area to be measured, and to slice the multichannel background noise waveform according to a preset time window to obtain multiple sub-window waveforms. The conversion module is used to perform a two-dimensional Fourier transform on each of the sub-window waveforms to obtain multiple data in the first frequency-wavenumber domain, and to perform normalization processing on each of the data in the first frequency-wavenumber domain based on the median truncation method to obtain multiple data in the second frequency-wavenumber domain. The clustering module is used to perform multi-stage clustering processing on multiple second frequency-wavenumber domain data to obtain multiple surface wave windows with coherent features and different modes. The clustering module is further configured to: perform a first-stage clustering process on the multiple second frequency-wavenumber domain data, and determine the frequency band range of waveforms with coherent characteristics based on the first-stage clustering results; perform filtering processing on the multiple second frequency-wavenumber domain data based on the frequency band range, perform a second-stage clustering process on the filtered frequency-wavenumber domain data, and obtain multiple surface wave windows with coherent characteristics based on the second-stage clustering results; and perform a third-stage clustering process on the multiple surface waves with coherent modes, and obtain multiple surface wave windows with coherent characteristics and different modes based on the third-stage clustering results. The urban environmental noise source screening device also includes: The dispersion energy map generation module is used to: perform seismic interferometry on each surface wave window with coherent characteristics and different modes to obtain a virtual shot gather corresponding to each surface wave window; superimpose multiple virtual shot gathers to obtain superimposed virtual shot gather data; and image the superimposed virtual shot gather data using the phase shift method to obtain a dispersion energy map.
6. A storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the method for screening urban environmental noise sources as described in any one of claims 1-4.
7. A computer device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the method for screening urban environmental noise sources as described in any one of claims 1-4.